- Introduction to Advanced Machine Learning on Google Cloud
- This module previews the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud.
- Architecting Production ML Systems
- This module explores what else a production ML system needs to do and how to meet those needs. You review how to make important, high-level, design decisions around training and model serving need to make in order to get the right performance profile for your model.
- Designing Adaptable ML Systems
- In this module, you learn how to recognize the ways that our model is dependent on our data, make cost-conscious engineering decisions, know when to roll back our models to earlier versions, debug the causes of observed model behavior and implement a pipeline that is immune to one type of dependency.
- Designing High-Performance ML Systems
- In this module, you identify performance considerations for machine learning models.
Machine learning models are not all identical. For some models, you focus on improving I/O performance, and on others, you focus on squeezing out more computational speed.
- Building Hybrid ML Systems
- Understand the tools and systems available and when to leverage hybrid machine learning models.
- Summary
- This module reviews what you learned in this course.
